Archie Mayani currently serves as the Chief Product Officer at GHX, a prominent global supply chain organization that specializes in using advanced data solutions and cloud-based technologies to create seamless communication and operational efficiency between healthcare providers—such as complex hospital systems—and the broad network of suppliers that support them. For over two decades, Mayani has consistently dedicated his career to addressing challenges at the intersection of healthcare delivery and supply chain logistics. His professional journey has included leadership roles within highly influential organizations such as Change Healthcare and the UnitedHealth Group, where he helped design, refine, and deploy clinical technologies as well as sophisticated systems for supply chain management within health-related enterprises.
At GHX, his efforts are directed toward ensuring that the company continuously develops and implements transformative technologies that allow hospitals to secure critical medical supplies—including life-sustaining materials such as intravenous fluids, implants, and other essential patient-care equipment—in the most efficient, reliable, and streamlined manner possible. Through the application of artificial intelligence, particularly models designed to both forecast and mitigate supply-chain disruptions, his team enables hospitals to classify the severity of these interruptions, identify which issues warrant immediate response, and suggest viable alternatives or substitutes when essential items are not available. These capabilities ultimately ensure that clinical teams remain properly equipped to deliver safe, timely, and effective medical treatment.
In a conversation with Business Insider, Mayani provided reflections on the unique complexities that distinguish healthcare’s adoption of AI technologies from that of other industries. During the interview—edited for clarity and conciseness—Rachel Somerstein posed questions that explored both his professional philosophy and the practical realities of implementing AI in such a critical sector.
When asked what differentiates healthcare from other industries, Mayani pointed out that many technology hubs, particularly in Silicon Valley, thrive on a “fail fast” mentality that emphasizes rapid experimentation and iteration. While such an approach may be tolerable or even humorous in low-stakes domains—for instance, when an algorithm in a dating application produces improbable or nonsensical matches—healthcare operates under an entirely different standard. In this field, the consequences of technological missteps can be profoundly serious. If a patient is on the operating table and a necessary device or medication has not arrived due to an avoidable supply chain failure, the outcome could shift from inconvenient to life-threatening. AI, therefore, must be introduced with far greater caution and responsibility in medicine than in nearly any other sector.
When discussing the overarching goals of AI in healthcare supply chain management, Mayani underscored the principle that patient safety must remain paramount and non-negotiable. In his view, the supply chain functions as an unseen yet indispensable operating system that silently supports the broader ecosystem of caregiving. GHX’s mission, therefore, revolves not simply around streamlining operations but around ensuring that every patient receives the right materials, at precisely the right time, thereby enhancing both the affordability and quality of care.
Reflecting on his career trajectory, Mayani explained that GHX has been leveraging AI and machine learning for over 15 years, a journey that accelerated during the COVID-19 pandemic. During that crisis, ensuring visibility into disrupted supply flows was absolutely critical, as shortages had direct and immediate implications on patient safety. Emerging from the pandemic, the organization increasingly focused on developing models that anticipate “backorders”—scenarios where essential supplies may be delayed or halted altogether. Such disruptions could arise from global political instability, severe weather events, or even accidents as ordinary as a transport truck losing cargo on a freeway. The objective has been to create intelligent systems that can not only detect the likelihood of such shortages but also propose proximal substitutes drawn from nearby inventory, thereby mitigating risk before it impacts patient care.
When asked where AI is currently proving most effective in supply chain management, Mayani described GHX’s agile development methodology, in which real-time customer feedback is used to guide innovation. Clients voiced an important realization: while predicting disruptions was valuable, not all disruptions carried equal weight. A temporary shortage of adhesive bandages, for example, does not have the same severity as a shortage of intravenous fluids critical to sustaining patient life. This insight led GHX to incorporate the concept of “clinical sensitivity,” alongside tools such as confidence scoring, to tailor insights so that the disruptions flagged were genuinely relevant to providers’ most urgent clinical needs. In this way, predictive analytics does not merely alert organizations to any issue, but instead prioritizes disruptions most capable of influencing patient outcomes.
Looking ahead, Mayani envisions that AI’s future role within healthcare supply chain management will involve increasing automation, while still maintaining human oversight at crucial junctures. Current strategies emphasize AI-driven agents that can manage repetitive workflows, liberating humans to handle higher-level decision-making until confidence levels are sufficiently high to allow for greater autonomy. Another rapidly developing area involves “copilot environments”—interactive dashboards that contextualize and narrate the massive flows of supply chain data. For example, GHX’s “perfect order dashboard” not only aggregates data on supply timeliness and invoice accuracy but also enables users to interrogate that information conversationally, akin to interacting with modern large language models. With such tools, routine tasks that once required several hours—compiling supplier performance reports, drafting follow-up communications, and making data-driven operational adjustments—can now be completed within mere minutes.
When offering guidance to other leaders in similar roles, Mayani emphasized that one of the most valuable yet challenging skills in healthcare innovation is the ability to decline requests judiciously. In a domain where every situation feels urgent, prioritization becomes essential, as not all matters are equally critical to patient welfare. Unlike consumer-facing technology companies or startups that possess the freedom to experiment with frequent failures, healthcare organizations must allocate their energy wisely. This requires disciplined governance frameworks, rigorous attention to data security and privacy, and steadfast focus on innovations that meaningfully elevate care delivery while also making it more cost-effective. In his words, the central measure of success always returns to a singular question: does the technology ultimately help make patient care both more affordable and of the highest possible quality?
Sourse: https://www.businessinsider.com/how-ai-can-support-healthcare-supply-chains-with-predictive-tools-2025-9
